Ode to my Lenovo

Two weeks back, my trusty Lenovo Z51-70 laptop gave up the ghost. It was showing signs well before that; consistently dropping my network connection, lagging severely when saving files and most tellingly, a noticeable flicker on the monitor. Can't say it didn't give me fair warning when on Saturday, it refused to power on.

Now, that was a sentimental moment.

Rest well, my friend.

I first obtained this flat white beauty during a Christmas Sale in 2014, when I was about to begin a Specialist Diploma in Mobile Apps Development. While my company at the time had issued me a decent notebook, I didn't feel comfortable using company property for my personal enterprise. I had a personal laptop, but it was on its last legs. Thus, I went for an i5 Intel Core that would meet my programming needs.

2015 to 2017

2015 was the second semester of my Specialist Diploma in Mobile Apps Development. With Java being the operating language, I installed Eclipse and the Java Runtime Environment. I also had NodeJS installed, and wrote apps in jQuery Mobile to deploy onto the mobile phone. This would perhaps be the most serious and intensive period of using the Lenovo.

Mobile app development.

Somewhere along the way, in addition to my steady diet of HTML, CSS and JavaScript experimentation, I experimented with things QBasic, LESS and AngularJS. An odd mix, I know, but I was having fun.

MeteorJS came along for me in early 2017. The lessons I learned here would help me years later when I experimented with ReactJS.

2017 to 2019

This was the period of my life where I settled down into a regular nine-to-five. I still experimented on weekends, writing D3 and ReactJS code. It was also the time I picked up Ruby and Rails, for whatever reason.

Like a stallion.

The Lenovo handled it all like a champ. It was tireless, inexhaustible, like a stallion in its prime.

Around the latter half of this period, I started working on my ACTA. Whatever work I needed done, I did it on my trusty laptop.

At some point during this period, I also got married. Late nights at the console were no longer viable, not if I wanted to sleep in the same bed as my wife. Still, I squeezed in whatever time I could.

2019 to 2023

This was the year the COVID-19 pandemic hit, and marked one of the longest durations my wife and I were geographically separated. During this time, my work on the Lenovo intensified, with me trying out Highcharts and VueJS.

During this period, however, I had acquired a MacBook and was gradually transitioning away from my faithful Lenovo which was starting to show its years.

Getting pretty old.

It was also when I embarked on my yet another Specialist Diploma, this time in Data Analytics. The MacBook handled Python and Tableau, but only my Lenovo could run Spotfire and  Power BI. It creaked and groaned, but ultimately delivered.

Why is this particular laptop special?

Why do I feel so strongly about this fella? It's not the first laptop to die on me, after all. I've lived more than forty years. But the last nine years of my professional life have been pretty tumultuous, and also represented the bulk of my career advancement and tech education, spanning two Specialist Diplomas and a Higher Certificate. This Lenovo was with me through all of it. I played no games on it. All I did was write code, and run code. (Some of that code were games I wrote, but we're just splitting hairs at that point!)

Much of whatever important files I have are saved on the cloud - on Google Drive, GitHub and Trello. Thankfully, I can resume my work on a new machine with little trouble. But I'll always treasure my time with this machine.

Rest In PC,
T___T

A Year of Learning Data Analytics (Part 1/2)

During the COVID-19 pandemic that shook the world, people underwent varying degrees of distress and discomfort. Consequently, we all coped in different ways. I, personally, in addition to working from home and enduring lockdowns, was putting up with being separated from Mrs TeochewThunder. I coped by keeping myself really busy.

More specifically, I signed up for yet another Specialist Diploma. This one was in Data Analytics.

No, I'm not some kind of masochist. But as a software developer, it was a professional obligation on my part to take an interest, if not be some kind of Jedi Master, in the various ways new skills could be picked up and applied. Also, I was past 40 at this point and the financial burden of taking up these courses was made negligible by generous subsidies from the Singapore government.

And what better time to do this, than at a time I didn't really have much else going on? What else would I be doing with my time - planning overseas vacations I couldn't go to? It wasn't like FOMO was going to kick in here. Also, as mentioned before in a prior blogpost, I had been exploring Data Visualization and this looked like a good direction to continue.

So yep. I bit the bullet.

Lessons delivered
via video call.

After all the messy administration work, the October of 2020 arrived and it was time to start learning shit. Since this was the COVID-19 pandemic, there would be no exams. All lessons would be delivered via video calls and we would be assessed on coursework such as school projects and weekly assignments. Another small mercy, because the biggest pain in the ass I remember from past experience was all the traveling to and from school and office. Now with strict lockdowns imposed due to the pandemic, no more of this crap!

Learning Python

The first term started fairly simple. It involved one of my favorite activities - learning a new programming language. This language was Python, and while I had dabbled with it in the past, now I had actual professional guidance. On my MacBook, I installed Jupyter and that was my IDE.

Programming in Python.

Of course, we had to deal with the usual basic stuff like operators, logic blocks and iteration. But once we got past that, wow, Python's data structures were really something. I'm talking lists, arrays, dictionaries, series, datasets, the works. And at the end of it, by the time we got to HTML scraping using Python libraries, I was convinced. Python is some seriously good shit.

Not having to hunt for missing semi-colons and do curly brackets, was a nice change. It didn't even bother me too much that Python insists on proper indentation - I do that all the time anyway. Generally, just in terms of syntax alone, Python gave me almost as much pleasure as Ruby.

Learning Data Visualization

As mentioned earlier, Data Visualization was one of the areas I was looking to expand on. My instructors went one better - they delved into storytelling. I had expected maybe a little bit of D3 or some frontend code or other. Instead, I was given Data Visualization software to work with.

Dashboards and storytelling.

This was Tableau Desktop, and it made my work a whole lot easier, especially when you consider that it wasn't just Data Visualization, there was also the storytelling part I had to contend with. I was also shown a whole new world of storytelling with regard to data - how other people did it, and all the fascinating examples of how one could visually represent data to present a narrative.

There was plenty more to the science of storytelling, but all that can be explored at another time.

Next

A look at the second term.

How I Learned Data Visualization

Recent years have seen me dwelve into Data Analytics. But even before that, I was exploring Data Visualization.

It all began when I ran some experiments with HTML, CSS and JavaScript to produce bar charts. That was straightforward enough, though line charts and pie charts took a lot more creativity.

HTML//CSS/JavaScript projects.

During one of my job interviews, I came across something in the list of required skills - D3 or Highcharts. I did not pass that interview, but after seeing that requirement in other job descriptions, I got curious enough to look it up. What I found blew my mind.

Exploring D3

D3 was the first thing I explored on the official website. Half of the library functions did not make sense to me and I have never been great at reading documentation. Thus, there was one thing left to do. I copied the example code and pasted it into a script, then ran it. Then I started changing the code, finding out what would happen when I commented out certain lines, or changed certain numbers. With my findings, I now knew what to look up on the documentation.

D3 projects

Within a week of practice, I was able to replicate the results of my earlier experiments, but now using D3. And then I wrote web tutorials pertaining to creating that code, because teaching code is a great way to cement that knowledge in your brain.

My experimentation was stalled somewhat due to the fact that exploring D3 naturally brought me to explore SVG as well. This was fun as well, but only peripherally related to Data Visualization.

Exploring Highcharts

This portion of my exploration was delayed due to several reasons - struggles with the chaos caused in the wake of the COVID-19 pandemic, experimentation in other areas and generally, bigger fish to fry. Yet, get to it I did eventually. Again, it began with the documentation in the official website, example code and a lot of trial and error before I produced my first web tutorial.

Highcharts project

At the time of this writing, I have yet to produce as extensively as I have done with D3. The only thing I have produced is a column chart in the same vein of what I had produced earlier with D3 and old-fashioned HTML, CSS and JavaScript... yet it was so easy that I suspect that even if I were to produce as many projects, it would not take me a lot of time.

I will sing the praises of Highcharts some other time.

Exploring other Data Visualization tools

Harkening back to almost the whole of 2021, I undertook a course in Data Analytics, an experience of which I will speak of another day. Suffice to say, part of the course covered Data Visualization. To whit, I was introduced to Data Visualization software which I had never encountered prior to this, such as Tableau, Spotfire and Power BI.

Data Visualization tools

It opened my eyes. Now I was no longer writing code to display the data; it was already being handled by these tools. Instead, I was doing the work prior to the Data Visualization. The cleaning. The reorganization. Making things make sense.

In other words, I was working on another level of the Data Visualization. I won't pretend that it wasn't sometimes mind-numbingly boring work, but it did give me a greater appreciation of the process.

All in all...

My little dive into Data Visualization was mostly brought on by professional curiosity. A curiosity that was in turn triggered by my almost obsessive need to know what things my industry wants of its professionals. But all the knowledge I have gained would not have been possible had I not followed up with the work required. This sounds like a lot of patting myself on the back, but what I really want to say is - stay curious, stay committed. It'll go a long way.

Serious as a chart attack,
T___T

Software Review: Tableau Desktop

During an ongoing foray into the world of Data Analysis, I was introduced to a Data Visualization Tool known as Tableau Desktop. Prior to that, my experience with Data Visualization had been limited to less sophisticated means - via the use of libraries such as D3; or worse, writing code without using libraries.

Tableau takes care of all the scaffolding required to produce passably decent-looking visualizations, and more.

It was picked up by Salesforce in 2019, and this bodes well for its longevity. Today, I'd like to provide a quick overview of what makes Tableau so attractive to a developer; or actually, just about anyone who needs a fuss-free way to analyze data beautifully.

The Premise

Tableau Desktop is used to manipulate one or more datasets, possibly joining them into a coherent whole, then using that data to create charts of several different types. From there, charts can be placed into dashboards, and in turn these dashboards can be used to form a Data Story.

Dashboard and Data Stories

More intricate work such as interactivity can be added via Actions. Some of the cool things you can do using Actions are - redirect to another dashboard, open a URL, change data filters on an existing sheet, and more!

Actions

Data may also be manipulated using built-in functions provided by Tableau.

Built-in functions

The Aesthetics

Tableau's color scheme, for the most part, is a very black and grey on a white background. That works for a clean look. Other colors come in once the charts are being created. By default, it's mostly a muted blue, but this can be customized.

A clean aesthetic.

The Experience

It's fairly intuitive most of the time. Emphasis on "most". For simple charts, it's easy as pie (heh heh). For the more complicated chart types, the process becomes exponentially more complex. Perhaps it's just me, but trial and error reap little reward as opposed to consulting online documentation. Thankfully, the online documentation and tutorials are plentiful.

Response is fast, but in all honesty, the datasets I was working with were fairly limited.

The Interface

The majority of the controls on a drag-and-drop basis, with other functionality available via right-clicks and double-clicks. Options from the main menu duplicate this functionality in case the user prefers that route.

Sometimes there may be a bit of coding, and the interface allows for it. But there is plenty that can still be done without writing a single line of code. Most of the time, code is only involved when you want to do things like custom calculated fields and dashboard interactivity.

Code interface

Additional levels of detail are available in menus upon right-clicking. There's a lot that is customizable.

Many customizations

What I liked

Fast and responsive. I may have mentioned it before, but seeing data sort itself neatly into charts of various shapes without having to do much beyond drag and drop, is a beauty to behold.

Almost effortless. Tableau saves you the trouble of having to painstakingly set up data visualization, by taking care of most of the hard work. All that's really left for the user is to customize the look and feel... and even then it's not a lot of work because the default setting already looks pretty decent.

Huge variety of chart types. There are so many basic chart types, and variations on them. And quite a few I didn't even know existed.

Treemap

Bubble Chart

Dual-axis Chart

Pie Chart

Filled Area Plot

Grouped Bar Chart

Line Chart

Stacked Bar Chart

And while I might not use it much, I'd like to take a moment to gush about geographical data. Tableau has all that covered. If you define data as Geographical, it gives you visual representation on an actual map.

Geographical data

Country names

And if you further break it down into county level, it actually has updated information on that as well! This is what it looks like for Singapore! Awesome, right?!

County names

The different screen layout options for dashboards is a really nice touch. Tableau does have a nose for the essentials.

Layout for screen sizes

Online resources are vast. There will be times when you need help from more experienced users. It's out there, in spades. The official documentation, on its own, is already pretty comprehensive.

What I didn't

Tableau can be complicated. While the interface is pretty intuitive and most basic uses cases can be resolved with relative ease, beyond that, there are times where things get tricky. Putting together something like a stacked bar chart, for example, can be a matter of a ridiculous amount of trial and error. Thankfully, for such scenarios, the extensive online documentation covers it more often than not.

Tableau would really benefit from an "Idiot Mode" - some step-by-step interface where the user chooses the visualization required and the software provides a guide as to what to drag/drop, and where.

Conclusion

There are probably many Data Visualization software packages out there which I haven't had the pleasure of using (unless one counts Microsoft's Excel or Google Charts), so do bear in mind that my frame of reference is woefully limited. However, having had the experience of having to create visualizations without the use of such a tool, my experience with Tableau Desktop has been overwhelmingly positive.

My Rating

8.5 / 10

Give it a go; it'll bleau your mind!
T___T